code: def representation_tree(self):
      # If we're checkpointing the kernel, we'll use chunked _matmuls defined in LazyEvaluatedKernelTensor
      if beta_features.checkpoint_kernel.value():
          return super().representation_tree()
      # Otherwise, we'll evaluate the kernel (or at least its LinearOperator representation) and use its
      # representation
      else:
          return self.evaluate_kernel().representation_tree()
  
imports: import functools
  from typing import Callable
  import torch
  from linear_operator import LinearOperator, to_linear_operator
  from linear_operator.utils.getitem import _noop_index
  from ..None import beta_features, settings
  from ..utils import deprecation
  from ..utils.memoize import cached
label_desc: Use image node_id 17 for calling the LazyEvaluatedKernelTensor obj's underlying member method code with example usage: obj.representation_tree() and returns: super, self